End of training

README.md

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+---
+library_name: transformers
+language:
+- jpn
+license: mit
+base_model: pyannote/speaker-diarization-3.1
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- diarizers-community/callhome
+model-index:
+- name: speaker-segmentation-fine-tuned-hindi1
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# speaker-segmentation-fine-tuned-hindi1
+
+This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/callhome dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4409
+- Model Preparation Time: 0.0038
+- Der: 0.1421
+- False Alarm: 0.0241
+- Missed Detection: 0.0277
+- Confusion: 0.0903
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.001
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.4605        | 1.0   | 194  | 0.4802          | 0.0038                 | 0.1609 | 0.0247      | 0.0332           | 0.1030    |
+| 0.386         | 2.0   | 388  | 0.4500          | 0.0038                 | 0.1516 | 0.0221      | 0.0318           | 0.0976    |
+| 0.3711        | 3.0   | 582  | 0.4384          | 0.0038                 | 0.1447 | 0.0225      | 0.0291           | 0.0931    |
+| 0.3674        | 4.0   | 776  | 0.4407          | 0.0038                 | 0.1430 | 0.0240      | 0.0279           | 0.0911    |
+| 0.3413        | 5.0   | 970  | 0.4409          | 0.0038                 | 0.1421 | 0.0241      | 0.0277           | 0.0903    |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.5.0+cu121
+- Datasets 3.1.0
+- Tokenizers 0.19.1

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